Conversational AI Companies · ASR Model Training
ASR Model Training Data for Conversational AI Companies
Voice-bot and chat-plus-voice platforms deploying into Indian markets, where the gap between demo accuracy and live accuracy is a code-mixing problem. Building or fine-tuning speech recognition for Indian languages from scratch or from a multilingual base model.
- Buyer
- Conversational AI Companies
- Use case
- ASR Model Training
- Metric
- Word error rate overall and per dialect
Where the two meet
Bots trained on clean single-language data fail on real switching mid-utterance That is a asr model training problem, and it is solved by data shaped like this:
- Hundreds to thousands of hours of verbatim-transcribed speech
- Wide speaker diversity: age, gender, region, education, recording condition
- Speaker-disjoint train/dev/test splits
Your evaluation criteria
- Does the data include overlap, interruptions and backchannels?
- Are utterances collected over the same channel conditions as production?
- Is intent labelling done against your live taxonomy?
Metrics
- Word error rate overall and per dialect
- Entity error rate on names and numbers
- Code-switch token accuracy
Pitfalls
- Read-speech-only corpora that do not transfer to spontaneous audio
- Speaker leakage across splits inflating reported accuracy
- Normalised-only transcripts with the raw text discarded
Contract points
- Scenario confidentiality
- Right to reuse across bot versions
- Delivery in a format that drops into an existing pipeline
Frequently asked
What does a first engagement look like?
Usually a scoped pilot: one language, an evaluation set plus a first training batch, delivered in three to five weeks, followed by the full programme.
Can you match our existing vendor's schema?
Yes. Working to your schema avoids a conversion pass and keeps deliveries comparable across vendors.
How is provenance documented?
Per-item contributor records and consent mapped to IDs in the manifest.
Send your requirement
Language, volume, metric, deadline.